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Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi

1305-8991 2618-5997

Akademik Bilişim Vakfı

Lojistik Regresyonun Özellik Azaltma Teknikleri ile Gen Dizilimlerinin Sınıflandırılmasındaki Başarısı

The Success Of Logistic Regression With Feature Reduction Techniques On Microarray Gene Classification

Yengi

Yeliz

İlhan Omurca

Sevinç

06 24 2016

8 1 1 12 06 24 2016

2005

Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi

Gen dizilimlerinin sınıflandırılması, hastalıkların ön görülebilmesi veya teşhis edilebilmesinde çok önemli rol oynamaktadır. Bütün gen dizilimi üzerinde etkili bir sınıflandırma yapabilmek mümkün olmadığından sağlıklı bir sınıflandırma yapılabilmesi için gerekli bilgiyi içeren genlerin (özelliklerin) özellik azaltma algoritmaları ile ayıklanması önem taşımaktadır. Bu çalışmada, özellikleri azaltmak için sezgisel arama teknikleri, özellik azaltma yaklaşımları(filter, wrapper, vb.) gibi farklı yöntemler analiz edilerek ön işleme adımının daha etkin bir şekilde gerçekleştirilmesi; bunun sonucunda elde edilen veri kümelerinin LR (Lojistik Regresyon) ve SVM (Destek Vektör Makineleri) gibi güçlü sınıflandırma araçları ile daha etkin şekilde sınıflandırılması hedeflenmiştir. Makine öğrenmesinde güçlü bir sınıflandırıcı olarak kabul edilen LR sınıflandırıcısı, özellik eksiltme yöntemleri ile gen dizilimlerinin sınıflandırılmasında SVM kadar geçerli ve etkin sınıflama aracı haline gelmiştir.

DNA microarray classification is important todiscovery of differentially expressed genes betweennormal and diseased patients are a central researchproblem in bioinformatics. All the genes used in theexpression profile are not informative. Further, manyof them are redundant. A pre-processing step in orderto reduce the number of genes by feature selectionand still retaining best class prediction accuracy for the cla1ssifier is crucial for precise tumorclassification. In this study comparison between classprediction accuracy of two different classifiers, LR(Logistic Regression) and SVM (Support VectorMachines), was carried out using the best genesselect by wrapper and filter technique to use heuristicsearch methods. We conclude that LR together withheuristic search based feature selection is the asefficient as SVM to the microarray gene predictiontechniques.

Gen analizi makine öğrenmesi lojistik regresyon özellik azaltma SVM

Microarray analysis binary classification machine learning logistic regression feature reduction tumor analysis SVM

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